False positive repression: Data centric pipeline for object detection in brain MRI

Terzı, Ramazan; Azgınoglu, Nuh; Terzi, Duygu Sinanç

doi:10.1002/cpe.6821

Cited by 5 publications

(4 citation statements)

References 51 publications

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“…Gazi Brains 2020 is a new brain MRI dataset generated to meet the needs for qualified MRI datasets. Although a few studies have started to use this dataset [ 26 , 27 , 28 ], it was used for the first time for the in-domain TL. In this study, slices from 50 patients with HGGs were used.…”

Section: Methodsmentioning

confidence: 99%

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

Terzi

Azginoglu

2023

Diagnostics

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Transfer learning has gained importance in areas where there is a labeled data shortage. However, it is still controversial as to what extent natural image datasets as pre-training sources contribute scientifically to success in different fields, such as medical imaging. In this study, the effect of transfer learning for medical object detection was quantitatively compared using natural and medical image datasets. Within the scope of this study, transfer learning strategies based on five different weight initialization methods were discussed. A natural image dataset MS COCO and brain tumor dataset BraTS 2020 were used as the transfer learning source, and Gazi Brains 2020 was used for the target. Mask R-CNN was adopted as a deep learning architecture for its capability to effectively handle both object detection and segmentation tasks. The experimental results show that transfer learning from the medical image dataset was found to be 10% more successful and showed 24% better convergence performance than the MS COCO pre-trained model, although it contains fewer data. While the effect of data augmentation on the natural image pre-trained model was 5%, the same domain pre-trained model was measured as 2%. According to the most widely used object detection metric, transfer learning strategies using MS COCO weights and random weights showed the same object detection performance as data augmentation. The performance of the most effective strategies identified in the Mask R-CNN model was also tested with YOLOv8. Results showed that even if the amount of data is less than the natural dataset, in-domain transfer learning is more efficient than cross-domain transfer learning. Moreover, this study demonstrates the first use of the Gazi Brains 2020 dataset, which was generated to address the lack of labeled and qualified brain MRI data in the medical field for in-domain transfer learning. Thus, knowledge transfer was carried out from the deep neural network, which was trained with brain tumor data and tested on a different brain tumor dataset.

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Section: Methodsmentioning

confidence: 99%

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

Terzi

Azginoglu

2023

Diagnostics

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“…The dataset preparation process is visualized in Figure 1 . The same dataset preparation process was performed as proposed by Terzi et al [ 45 ]. Accordingly, each independent mask was defined as an object in a slice.…”

Section: Materials and Methodsmentioning

confidence: 99%

An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI

Terzı

2023

Diagnostics

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This paper proposes ensemble strategies for the deep learning object detection models carried out by combining the variants of a model and different models to enhance the anatomical and pathological object detection performance in brain MRI. In this study, with the help of the novel Gazi Brains 2020 dataset, five different anatomical parts and one pathological part that can be observed in brain MRI were identified, such as the region of interest, eye, optic nerves, lateral ventricles, third ventricle, and a whole tumor. Firstly, comprehensive benchmarking of the nine state-of-the-art object detection models was carried out to determine the capabilities of the models in detecting the anatomical and pathological parts. Then, four different ensemble strategies for nine object detectors were applied to boost the detection performance using the bounding box fusion technique. The ensemble of individual model variants increased the anatomical and pathological object detection performance by up to 10% in terms of the mean average precision (mAP). In addition, considering the class-based average precision (AP) value of the anatomical parts, an up to 18% AP improvement was achieved. Similarly, the ensemble strategy of the best different models outperformed the best individual model by 3.3% mAP. Additionally, while an up to 7% better FAUC, which is the area under the TPR vs. FPPI curve, was achieved on the Gazi Brains 2020 dataset, a 2% better FAUC score was obtained on the BraTS 2020 dataset. The proposed ensemble strategies were found to be much more efficient in finding the anatomical and pathological parts with a small number of anatomic objects, such as the optic nerve and third ventricle, and producing higher TPR values, especially at low FPPI values, compared to the best individual methods.

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“…7 Ramazan It has been observed that the proposed pipeline is a promising solution to reducing FP cases. 8 The guest editors hope that the research contributions and findings in this special issue would benefit the readers in enhancing their knowledge and encouraging them to work on various aspects of intelligent systems and applications.…”

Section: Editorialmentioning

confidence: 99%

“…FPR‐P is proposed within the scope of the study by comparing with the classical pipeline by two different datasets as Gazi Brains 2020 and BraTS 2020, and three different deep learning models, as Mask R‐CNN, EfficientDet, and YOLOv5. It has been observed that the proposed pipeline is a promising solution to reducing FP cases 8 …”

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confidence: 99%